Natural Language Generation:
Popular NLG tasks and dataset on RDF verbalzation, such as WebNLG[1]}, do not take into consideration the question elements when reconstructing the facts in natural language. Hence, the sentences generated involve relevant information from the facts without any context of the question. From this standpoint, our dataset would open up new paradigm in NLG research.

Question Answering and Dialog Systems:
Different QA datasets based on Text, such as SQuAD [2]} and NaturalQuestions [3]}, and KG, such as SimpleQuestion [4]}, WebQuestionSP [5]}, ComplexWebQuestions [6]}, QALD-9 [7]}, LC-QuAD 1.0 [8]}, LC-QuAD 2.0 [9]}, exists. Furthermore, datasets are also adapted into conversation or dialog format with multiple utterances such as, QuAC [10]}, CoQA [11]}, CSQA [12]}, SQA [13]}. Recent trend in QA datasets are based on the following aspects:
[(1)]

expanding the size of dataset,

extending the dataset with more variations in question type such as aggregations, boolean queries, etc.,

enhancing question complexity using compound features like comparison or unions,

enhancements covering multiple utterances in the form of conversations.

